Processing system responsive to analysis filter criteria

ABSTRACT

According to some embodiments, a historical request data store may contain electronic records representing historical requests and, for each historical request, a set of analysis variables including a request description, resource allocation data, and at least one outcome indication. An automated outcome tracker system computer may catalogue a subset of the electronic records, based on the at least one outcome indication for each electronic record, as representing positive outcomes. An operator terminal may provide an interactive graphical user interface display and a back-end application computer server may receive from the operator terminal a set of analysis filter criteria. The back-end application computer server may then calculate and display impactability scores. According to some embodiments, the computer server may also calculate and display negative outcome risk scores.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 15/161,582 entitled “PROCESSING SYSTEM RESPONSIVE TO ANALYSISFILTER CRITERIA” and filed May 23, 2016. The entire content of thatapplication is incorporated herein by reference.

BACKGROUND

An enterprise system may be tasked with responding to requests that arereceived from various sources. For example, an enterprise system mightneed to respond to hundreds of thousands of such requests on a periodicbasis. Note that the enterprise system might have a limited amount ofresources that can be used to efficiently respond to those requests. Asa result, the limited amount of resources may need to be allocated amongthe requests that are received by the enterprise system. In some cases,each request might be associated with various attribute characteristicvalues that might be used to help determine if resources could beefficiently allocated to that particular request. Manually reviewingeach request, however, can be a time consuming and error probetask—especially when there are a substantial number of requests and/orattribute characteristic values associated with each request. Moreover,various attribute characteristic values could have hidden relationshipsassociated with how effective an allocation of resources to that requestwill be, making manual review even more impractical.

It would be desirable to provide systems and methods to automaticallyimprove the allocation of resources for an enterprise system in a waythat provides faster, more effective recommendations and that allows forflexibility and effectiveness when determining and/or reviewing thoserecommendations.

SUMMARY OF THE INVENTION

According to some embodiments, systems, methods, apparatus, computerprogram code and means are provided to facilitate an allocation ofresources for an enterprise system via an automated back-end applicationcomputer server. In some embodiments, a historical request data storecontains electronic records representing a plurality of historicalrequests and, for each historical request, a set of analysis variablesincluding: a request description, resource allocation data, and at leastone outcome indication. An automated outcome tracker system computer isprogrammed to retrieve the electronic records from the historicalrequest data store and catalogue a subset of the electronic records,based on the at least one outcome indication for each electronic record,as representing positive outcomes. An operator terminal may be adaptedto provide an interactive graphical user interface display, and theautomated back-end application computer server may be programmed toreceive, from the operator terminal through a distributed communicationnetwork, a set of analysis filter criteria comprising indications of aplurality of analysis variables entered via the interactive graphicaluser interface display. The computer server may access the electronicrecords from the historical request data store and receive theinformation about the catalogued subset of electronic records from theautomated outcome tracker system computer. Based on the analysis filter,resource allocation analysis variables, and catalogued subset ofelectronic records, the computer server may calculate impactabilityscores. Indications of the impactability scores may then be transmittedto be provided via the interactive graphical user interface display.According to some embodiments, a communication port is coupled to theback-end application computer server to facilitate an exchange ofelectronic messages associated with the interactive graphical userinterface display via the distributed communication network. Accordingto some embodiments, computer server may also calculate (based on theanalysis filter, resource allocation analysis variables, and outcomeindications) and display negative outcome risk scores.

Some embodiments comprise: means for retrieving, by an automated outcometracker system computer from a historical request data store, electronicrecords representing a plurality of historical requests and, for eachhistorical request, a set of analysis variables including: a requestdescription, resource allocation data, and at least one outcomeindication; means for cataloguing, by the automated outcome trackersystem computer, a subset of the electronic records, based on the atleast one outcome indication for each electronic record, as representingpositive outcomes; means for outputting, from the automated outcometracker system computer, information about the catalogued subset ofelectronic records; means for receiving, by the automated back-endapplication computer server from a remote operator terminal through adistributed communication network, a set of analysis filter criteriacomprising indications of a plurality of analysis variables entered viathe interactive graphical user interface display; means for accessing,by the back-end application computer server, the electronic records fromthe historical request data store and receiving the information aboutthe catalogued subset of electronic records from the automated outcometracker system computer; based on the analysis filter, resourceallocation analysis variables, and catalogued subset of electronicrecords, means for calculating impactability scores; and means fortransmitting, from the back-end application computer server, indicationsof the impactability scores to be provided via the interactive graphicaluser interface display.

In some embodiments, a communication device associated with a back-endapplication computer server exchanges information with remote devices.The information may be exchanged, for example, via public and/orproprietary communication networks.

A technical effect of some embodiments of the invention is an improvedand/or computerized way to automatically improve the allocation ofresources for an enterprise system in a way that provides faster, moreeffective recommendations and that allows for flexibility andeffectiveness when determining and/or reviewing those recommendations.With these and other advantages and features that will becomehereinafter apparent, a more complete understanding of the nature of theinvention can be obtained by referring to the following detaileddescription and to the drawings appended hereto.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a high-level block diagram of a system according to someembodiments.

FIG. 2 illustrates a method according to some embodiments of the presentinvention.

FIG. 3 is a high-level block diagram of an insurance enterprise systemaccording to some embodiments.

FIG. 4 is a flow diagram associated with processes and criteriaassociated with predictive analytics accordance with some embodiments ofthe present invention.

FIG. 5 illustrates an approach to predictive analytics according to someembodiments.

FIGS. 6 through 10 illustrate interactive GUI displays for a clinicalanalytic scoring engine according to some embodiments.

FIG. 11 is a block diagram of an apparatus in accordance with someembodiments of the present invention.

FIG. 12 is a portion of a tabular claim resource allocation database inaccordance with some embodiments.

FIG. 13 is a portion of a tabular scoring database according to someembodiments.

FIG. 14 is a portion of a tabular rule database in accordance with someembodiments.

FIG. 15 illustrates a system having a predictive model in accordancewith some embodiments.

FIG. 16 illustrates a tablet computer displaying a resource allocationuser interface according to some embodiments.

FIG. 17 illustrates an overall enterprise workflow in accordance withsome embodiments.

DETAILED DESCRIPTION

The present invention provides significant technical improvements tofacilitate electronic messaging, predictive modeling, and dynamic dataprocessing. The present invention is directed to more than merely acomputer implementation of a routine or conventional activity previouslyknown in the industry as it significantly advances the technicalefficiency, access and/or accuracy of communications between devices byimplementing a specific new method and system as defined herein. Thepresent invention is a specific advancement in the area of resourceallocation by providing benefits in data accuracy, data availability,and data integrity, and such advances are not merely a longstandingcommercial practice. The present invention provides improvement beyond amere generic computer implementation as it involves the processing andconversion of significant amounts of data in a new beneficial manner aswell as the interaction of a variety of specialized client and/or thirdparty systems, networks and subsystems. For example, in the presentinvention information may be processed, forecast, and/or predicted via aback-end application server and results may then be analyzed efficientlyto evaluate the accuracy of various results and recommendations, thusimproving the overall performance of an enterprise system, includingmessage storage requirements and/or bandwidth considerations (e.g., byreducing the number of messages that need to be transmitted via anetwork). Moreover, embodiments associated with predictive models mightfurther improve performance values, predictions of performance values,resource allocation decisions, etc.

An enterprise system may be tasked with responding to requests that arereceived from various sources. For example, an enterprise system mightneed to respond to hundreds of thousands of such requests on a periodicbasis. Note that the enterprise system might have a limited amount ofresources that can be used to efficiently respond to those requests. Asa result, the limited amount of resources may need to be allocated amongthe requests that are received by the enterprise system. In some cases,each request might be associated with various attribute characteristicvalues that might be used to help determine if resources could beefficiently allocated to that particular request. Manually reviewingeach request, however, can be a time consuming and error probetask—especially when there are a substantial number of requests and/orattribute characteristic values associated with each request. Moreover,various attribute characteristic values could have hidden relationshipsassociated with how effective an allocation of resources to that requestwill be, making manual review even more impractical.

It would be desirable to provide systems and methods to facilitate anallocation of resources in a way that provides faster, more effectiverecommendations and that allows for flexibility and effectiveness whendetermining and/or reviewing those recommendations. FIG. 1 is ahigh-level block diagram of an enterprise system 100 according to someembodiments of the present invention. In particular, the system 100includes a back-end application computer server 150 and an outcometracker system 120 that may access information in a historical requestdata store 110 (e.g., storing a set of electronic records representingrequests associated with risk associations, each record including one ormore communication addresses, attribute variables, etc.). The back-endapplication computer server 150 may also exchange information with aremote operator terminal 130 (e.g., via a firewall 135). According tosome embodiments, a scoring engine 125 in the outcome tracker system 120may catalogue records in the historic request data store 110 as having“successful” or “unsuccessful” outcomes. Moreover, an analytics engine155 of the back-end application computer server 150 may facilitate dataprocessing, predictions, and/or the display of results via one or moreremote operator terminals 130. A resource allocation tool 160 mightreceive criteria from the back-end application computer server 150 (orfrom the operator terminal 130) and use that criteria to process recordsin a current request data store 170 and generate recommendations.Further note that the outcome tracker system 120, the back-endapplication computer server 150, and/or the resource allocation tool 160might be associated with a third party, such as a vendor that performs aservice for the enterprise system 100.

The outcome tracker system 120, the back-end application computer server150, resource allocation tool 160, operator terminal 130 might be, forexample, associated with a Personal Computer (“PC”), laptop computer,smartphone, an enterprise server, a server farm, and/or a database orsimilar storage devices. According to some embodiments, an “automated”outcome tracker system 120, back-end application computer server 150,and/or resource allocation tool 160 may facilitate data processingassociated with an allocation of resources based on electronic recordsin the historical request data store 110 and current request data store170. As used herein, the term “automated” may refer to, for example,actions that can be performed with little (or no) intervention by ahuman.

As used herein, devices, including those associated with the back-endapplication computer server 150 and any other device described hereinmay exchange information via any communication network which may be oneor more of a Local Area Network (“LAN”), a Metropolitan Area Network(“MAN”), a Wide Area Network (“WAN”), a proprietary network, a PublicSwitched Telephone Network (“PSTN”), a Wireless Application Protocol(“WAP”) network, a Bluetooth network, a wireless LAN network, and/or anInternet Protocol (“IP”) network such as the Internet, an intranet, oran extranet. Note that any devices described herein may communicate viaone or more such communication networks.

The back-end application computer server 150 may store information intoand/or retrieve information from the historical request data store 110.The historical request data store 110 might, for example, storeelectronic records representing a plurality of requests associated withrisk associations, each electronic record having a set of attributevalues. The historical request data store 110 may also containinformation about past and current interactions with parties, includingthose associated with remote communication devices. The historicalrequest data store 110 may be locally stored or reside remote from theback-end application computer server 150 and/or the outcome trackersystem 120. As will be described further below, the historical requestdata store 110 may be used by the back-end application computer server150 to facilitate an allocation of resources. Although a single back-endapplication computer server 150 is shown in FIG. 1, any number of suchdevices (and other elements) may be included. Moreover, various devicesdescribed herein might be combined according to embodiments of thepresent invention. For example, in some embodiments, the back-endapplication computer server 150, the outcome tracker system 120, and/orthe resource allocation tool 160 might be co-located and/or may comprisea single apparatus.

According to some embodiments, the enterprise system 100 mayautomatically facilitate an allocation of resources via the automatedback-end application computer server 150. For example, at (1) theoutcome tracker system 120 may access information in the historicalrequest data store 110 and use the scoring engine 125 to cataloguerecords as representing positive outcomes. The information aboutpositive outcomes may be transmitted to the back-end applicationcomputer server 150 at (2). At (3), a user at an operator terminal 130may provide a set of filter criteria to the back-end applicationcomputer server 150. The analytics engine 155 may then use the receivedinformation to calculate results to be provided to the operator terminal130 at (4). Promising criteria may then be provided to the resourceallocation tool 160 (either from the operator terminal 130 or theback-end application computer server 150) at (5). The resourceallocation tool 160 may then access information in the current requestdata store 170 at (6) and use the appropriate criteria to generateappropriate recommendations at (7).

Note that the system 100 of FIG. 1 is provided only as an example, andembodiments may be associated with additional elements or components.According to some embodiments, the elements of the enterprise system 100automatically facilitate an allocation of resources using a distributedcommunication network. FIG. 2 illustrates a method 200 that might beperformed by some or all of the elements of the system 100 describedwith respect to FIG. 1, or any other system, according to someembodiments of the present invention. The flow charts described hereindo not imply a fixed order to the steps, and embodiments of the presentinvention may be practiced in any order that is practicable. Note thatany of the methods described herein may be performed by hardware,software, or any combination of these approaches. For example, acomputer-readable storage medium may store thereon instructions thatwhen executed by a machine result in performance according to any of theembodiments described herein.

At S210, the system may retrieve, from a historical request data store,electronic records representing a plurality of historical requests and,for each historical request, a set of analysis variables including arequest description, resource allocation data, and at least one outcomeindication. At S220, an automated outcome tracker system computer mayretrieve the electronic records from the historical request data storeand catalogue a subset of the electronic records, based on the at leastone outcome indication for each electronic record, as representingpositive outcomes. At S230, the outcome tracker system may outputinformation about the catalogued subset of electronic records.

According to some embodiments, an operator terminal may be adapted toprovide an interactive Graphical User Interface (“GUI”) display. AtS240, an automated back-end application computer server, remote from theoperator terminal and coupled to the automated outcome tracker systemcomputer and the operator terminal, may receive, from the operatorterminal through a distributed communication network, a set of analysisfilter criteria comprising indications of a plurality of analysisvariables entered via the interactive GUI display.

At S250, the back-end application computer server may access theelectronic records from the historical request data store and receivethe information about the catalogued subset of electronic records fromthe automated outcome tracker system computer. Based on the analysisfilter, resource allocation analysis variables, and catalogued subset ofelectronic records, the back-end application computer server maycalculate impactability scores at S260. For example, records with lowimpactability scores may be unlikely to benefit from an allocation ofresources (e.g., there is little hope the resources will be effective).

At S270, the back-end application computer server may transmitindications of the impactability scores to be provided via theinteractive GUI display. According to some embodiments, a communicationport coupled to the back-end application computer server may facilitatean exchange of electronic messages associated with the interactive GUIdisplay via the distributed communication network. In some embodiments,the computer server may also calculate (based on the analysis filter,resource allocation analysis variables, and outcome indications)negative outcome risk scores. Note that records with relatively highnegative outcome risk scores might especially benefit from an allocationof resources (e.g., because the eventual negative outcome might beavoided). According to some embodiments, a risk allocation tool computermay receive the set of analysis filter criteria as meeting apre-determined performance threshold. The risk allocation tool computermay then receive electronic records representing a plurality of currentrequests and, for each historical request, a set of analysis variables,and automatically output a resource allocation recommendation for theenterprise system in connection with each current request based on theset of analysis filter criteria. In this way, an operator might exploredifferent subset of records and how they each might potentially benefitfrom an allocation of resources.

Note that the embodiments described with respect to FIGS. 1 and 2 may beapplicable to many different types of enterprise systems. For example,FIG. 3 is a high-level block diagram of a Short Term Disability (“STD”)group benefits insurance enterprise system 300 according to someembodiments. As before, the insurance system 300 includes a back-endapplication computer server 350 and a case outcome tracker system 320that may access information in a historical insurance data store 310(e.g., storing a set of electronic records representing STD insuranceclaims associated with group benefits insurance policies, each recordincluding one or more communication addresses, attribute variables,etc.). The back-end application computer server 350 may also exchangeinformation with a remote interactive GUI display 330. According to someembodiments, a scoring engine 325 in the case outcome tracker system 320may catalogue records in the historic insurance claim data store 310 ashaving “successful” or “unsuccessful” outcomes. As used herein, thephrase “successful outcome” might refer to, for example, a clinicalresolution or success rate identified by process owners of each clinicalprocess as being a subset of outcomes for each clinical resource type tobe denoted as a success from a clinical standpoint.

Moreover, a case analytics engine 355 of the back-end applicationcomputer server 350 may facilitate data processing, predictions, and/orthe display of results via one or more remote interactive GUI displays330. A medical case manager, behavioral health case manager, and returnto work coordinator allocation tool 360 might receive criteria from theback-end application computer server 350 (or from the interactive GUIdisplay 330) and use that criteria to process records in a currentinsurance claim data store 370 and generate recommendations. Furthernote that the case outcome tracker system 320, the back-end applicationcomputer server 350, and/or the medical case manager, behavioral healthcase manager, and return to work coordinator allocation tool 360 mightbe associated with a third party, such as a vendor that performs aservice for the enterprise system 300.

According to some embodiments, the insurance enterprise system 300 isassociated with a group benefits insurance system and the historicalrequests represent STD insurance claims. In this case, the requestdescription in the historical insurance claim data store 310 (and thecurrent insurance claim data store 370) might be associated with, forexample, a diagnosis category, a diagnosis code, and/or a surgicalprocedure code. Moreover, the at least one outcome indication stored foreach STD claim might be associated with a disability duration. Otherinformation in the set of analysis variables might include: an age atdate of disability (e.g., a numerical value in years, a flag indicatingthe claimant is over 40 years old, etc.), a job class (e.g., a numericalrating, a “light,” “medium,” or “heavy” category, etc.), a claim volume,a claim complexity segment, a detailed description of a primarydisabling diagnosis, a medical diagnosis code, an indication of whethera diagnosis is highly subjective, an indication of multiple diagnoses, asurgery code, gender, an indication of a musculoskeletal diagnosis, anindication of a diabetes diagnosis, a substance abuse diagnosis, ahypertension diagnosis, a lower limb fracture diagnosis, an inability tocontact claimant, an indication claimant has employment issues (e.g.,was the claimant fired, was the claimant subject to disciplinaryactions, etc.), a prior history of claims, a hiring date, and/or aretirement date.

According to some embodiments, the case analytics engine 355 calculatesan “impactability score” based on the set of filter criteria receivedfrom the interactive GUI display 330. As used herein, the phrase“impactability score” might refer to, for example, a relative measure ofclinical success for a given resource type on a given set of claims.This might be calculated, for example, by dividing a clinical resolutionrate for a given set of claims by an overall clinical resolution rateacross all claims. The result might be, for example, a number that isnormalized to 1. According to some embodiments, impactability scores maybe associated with medical case managers, behavioral health casemanagers, and/or return to work coordinators.

The case analytics engine 355 may also calculate a “negative outcomerisk score” based on the set of filter criteria received from theinteractive GUI display 330. The “negative outcome risk score” mightindicate a relative likelihood of a claim (in a given group) eventuallytransitioning to a Long Term Disability (“LTD”) insurance claim. Thismay be calculated, for example, by dividing the percentage of claims ina given group that transition to LTD by the percentage of all STD claimsthat transition to LTD. Another value that might be calculated by thecase analytics engine 355 is “volatility” which may be associated with arelative measure of predictability (or lack thereof) in a given group ofclaims. This might be calculated by dividing a standard deviation ofbenefit duration days (for a given group) by the standard deviation ofthe benefit duration days on all STD claims. Still another value thatmight be calculated by the case analytics engine 355 is “duration risk”which may be associated with a measure of severity that indicates arelative risk of a claim (in a given group) exceeding an expectedduration by 14 days or more, with an expected duration established byactuarial. This might be calculated, for example, by dividing aprobability of a claim (in a given group) exceeding duration by 14 daysor more by the percentage of all STD claims that exceed duration by 14days or more. Still other examples of values that might be calculated bythe automated back-end application computer server 350 include: a medianclaim duration, a mean claim duration, a total number of interventions,interventions as a percent of claim volume, successes as a percentage ofinterventions, a mean number of paid benefit days, a median number ofpaid benefit days, and a standard deviation of claim duration days.

According to some embodiments, the automated back-end applicationcomputer server 350 is further programmed to receive, from theinteractive GUI display 330 through a distributed communication network(e.g., the Internet), an adjusted set of analysis filter criteria. Thecomputer server 350 may then re-calculate the impactability and negativeoutcome risk scores based on the adjusted set of analysis filtercriteria and transmit indications of the re-calculated scores to beprovided via the interactive GUI display 330.

In this way, the insurance enterprise system 300 may be used to exploreand analyze information in the historical insurance claim data store 310in a predictive manner. For example, FIG. 4 is a flow diagram 400associated with processes and criteria associated with predictiveanalytics accordance with some embodiments of the present invention. At410, high opportunity groups of claims may be identified using apredictive model for proactive intervention. Selection criteria mightinclude, for example, a likelihood of exceeding expected duration(associated with risk), a likelihood of transitioning to a LTD claim, acurrent clinical success rate (associated with impactability), andclinical expertise (whether or not the model makes sense from a clinicalstandpoint). At 420, the proactive intervention might be tried in apilot program on a selected group of STD insurance claims. At 430, theresults of that pilot program may be analyzed. If it is determined at440 both that (1) improved outcomes were achieved and (2) the successrate was greater than a baseline level of success, the pilot programmight be automated (e.g., and implemented full-scale for all future STDinsurance claims). In some cases, the flow diagram might be exited atthis point (as illustrated by the dashed arrow in FIG. 4). If it isdetermined at 450 both that (1) improved outcomes were achieved and (2)the success rate was not greater than a baseline level of success, thepilot program might be analyzed and refined (e.g., in an attempt toachieve improved results). A new set of claims might be determined at460 and the entire process may be repeated at 410.

FIG. 5 illustrates an approach 500 to predictive analytics according tosome embodiments. In particular, the approach 500 leverages clinicalexpertise 510 alongside predictive analytics 520 to provide a culture ofcontinuous improvement (e.g., via a test and refine process 530). As aresult, significant and sustained improvements may be achieved viaproactive clinical intervention on high-risk, high-opportunity STDinsurance claims at the intersection 540 of the clinical expertise 510,predictive analytics 520, and test and refine process 530.

To help identify such claims, FIG. 6 illustrates an interactive GUIdisplay 600 including visually perceptible elements for a clinicalanalytic scoring engine according to some embodiments. The display 600includes a “select analysis variables” 610 area when an operator candefine input variables to test and analyze. Such inputs might include,for example, a major STD disabling diagnosis category, a detaileddescription of a primary disabling diagnosis, an InternationalStatistical Classification (“ICD”) medical diagnosis code (e.g., analpha-numeric code that classifies diseases and a wide variety of signs,symptoms, abnormal findings, complaints, social circumstances andexternal causes of injury or disease), a binary variable indicatingwhether or not a diagnosis is “highly subjective,” whether a claimanthave multiple diagnoses, a surgery code, a job class, a customeraccount, the claimant's gender, a claim segment level, claimant ageinformation (e.g., is the claimant older than 61?), a secondarydiagnosis, a tertiary diagnosis, an indication of breast cancer, anindication of musculoskeletal diagnosis, an indication of diabetes, anindication of substance abuse, an indication of depression, anindication of hypertension, an indication of lower limb fracture, anindication the insurance enterprise is unable to contact the claimant,an indication that the claimant had a disciplinary issue at work, anindication the claimant a disgruntle employee, an indication that thereare inconsistent details in the claim file, an indication that theclaimant has difficult circumstances in life outside of work, anindication that the claimant had a prior history of claims, anindication that the claimant was recently hired, an indication that theclaimant plans to retire soon, and/or an indication that the claimantuncooperative.

The display 600 also includes a “select columns to view” 620 area whenan operator can define output variables. Such outputs might include, forexample, normalized impactability scores 630 and LTD risk scores 640.Other examples of output variables might include, for example, a totalnumber of annual claims, a total number of interventions by medical casemanagers, a number of interventions as a percent of claim volume, anumber of successful interventions as defined by the insuranceenterprise, a number of successes as a percentage of interventions, atotal number of interventions by return to work coordinators orrehabilitation case managers, a total number of interventions bybehavioral health case managers, mean claim durations days, median claimduration days, mean number of paid benefit days, median number of paidbenefit days, standard deviation of the claims duration days, percentageof claims that exceed their expected duration by 14 days or more, volumeof claims that transition into LTD, percentage of claims that transitioninto LTD, relative variability in claim durations for a specified groupof claims, compared to the overall population of claims, relative riskof a claim in a specified group exceeding the expected duration by 14days or more, and/or relative risk of a claim in a specified grouptransitioning into LTD. The display 600 may further include a downloadicon 650 (e.g., selectable to save an electronic file of results, suchas a comma separated values file) and/or a search entry 660 area (e.g.,where an operator can enter one or more search terms).

As illustrated in FIG. 7, a display 700 may further include an “enterminimum claim volumes (annual)” 710 area where an operator can define athreshold number of claims. For example, an operator might only beinterested in results associated with at least a particular number ofclaims. Similar, the display might include a minimum medical casemanager number of claims 720 area and/or a minimum return to workcoordinator number of claims 730. According to some embodiments, anoperator might also enter values 740 to search and/or re-order resultsto be analyzed. As illustrated in FIG. 8, a display 800 may utilize adrop-down menu to “select columns to view” 810. In this way, an operatormight indicate that he or she is interested in particular types ofinterventions, resolution rates, etc. For example, the display 900 inFIG. 9 illustrates an operator who is particularly interested in a“musculoskeletal” 910 diagnosis category. Moreover, the results indicatethat the normalized return to work coordinator impactability score 920is “1.80” (which might indicate that this type of injury mightespecially benefit from this type of resource allocation).

By way of example, an insurance enterprise might use a clinical analyticscoring engine tool to generate groups of STD group benefit claims forproactive clinical intervention using as follows. A group of cliniciansmight brainstorm hypotheses around which groups and types of claimswould benefit from increased rates of clinical intervention. The groupmay validate the hypotheses in real time, identifying selected groups ofclaims where the impactability scores for at least one type of clinicianis over 1.0 and the duration or LTD risk score is also over 1.0. Forexample, the clinicians might identify lower limb fractures as apotential claim group that could benefit from increased involvement fromreturn to work coordinators. The clinicians might identify the followingset of criteria using the tool:

-   -   Age at Date of Disability>=35    -   Job Class=1 OR 2    -   STD Diagnosis Category=“Fracture/Injury Lower Limb”    -   Surgery=“Yes”

As shown by the display 1000 in FIG. 10, this group has a highlikelihood of going into LTD (LTD risk 1030 of “2.25”), which can makeit more difficult for the claimant to eventually return to employment.Additionally, the medical case mangers impactability 1010 (“1.50”) andreturn to work coordinators impactability 1020 (“2.00”) are relativelyhigh, indicating they have historically been able to positively impactoutcomes on these types of claims. This group of claims, along withother groups identified using this process, may then be automaticallypushed to the appropriate clinical resource in a pilot initiative. Theresults are tracked and analyzed. If the successes rate (i.e.,resolution rate) is higher than a baseline success rate for a given typeof clinical resource (e.g., 20%) the identified group of claims is putinto an automated referral process as a go-forward strategy.

The embodiments described herein may be implemented using any number ofdifferent hardware configurations. For example, FIG. 11 illustrates aback-end application computer server 1100 that may be, for example,associated with the systems 100, 300 of FIGS. 1 and 3. The back-endapplication computer server 1100 comprises a processor 1110, such as oneor more commercially available Central Processing Units (“CPUs”) in theform of one-chip microprocessors, coupled to a communication device 1120configured to communicate via a communication network (not shown in FIG.11). The communication device 1120 may be used to communicate, forexample, with one or more remote operator terminals and or communicationdevices (e.g., PCs and smartphones). Note that communications exchangedvia the communication device 1120 may utilize security features, such asthose between a public internet user and an internal network of theinsurance enterprise. The security features might be associated with,for example, web servers, firewalls, and/or PCI infrastructure. Theback-end application computer server 1100 further includes an inputdevice 1140 (e.g., a mouse and/or keyboard to enter information aboutpast insurance claims, predictive models, etc.) and an output device1150 (e.g., to output reports regarding system administration and/orpredictive analytic results).

The processor 1110 also communicates with a storage device 1130. Thestorage device 1130 may comprise any appropriate information storagedevice, including combinations of magnetic storage devices (e.g., a harddisk drive), optical storage devices, mobile telephones, and/orsemiconductor memory devices. The storage device 1130 stores a program1115 and/or a scoring tool or application for controlling the processor1110. The processor 1110 performs instructions of the program 1115, andthereby operates in accordance with any of the embodiments describedherein. For example, the processor 1110 may access a historical requestdata store that contains electronic records representing historicalrequests and, for each historical request, a set of analysis variablesincluding a request description, resource allocation data, and at leastone outcome indication. The processor 1110 may catalogue a subset of theelectronic records, based on the at least one outcome indication foreach electronic record, as representing positive outcomes. An operatorterminal may provide an interactive GUI display and the processor 1110receive from the operator terminal a set of analysis filter criteria.The processor 1110 may then calculate impactability and negative outcomerisk scores.

The program 1115 may be stored in a compressed, uncompiled and/orencrypted format. The program 1115 may furthermore include other programelements, such as an operating system, a database management system,and/or device drivers used by the processor 1110 to interface withperipheral devices.

As used herein, information may be “received” by or “transmitted” to,for example: (i) the back-end application computer server 1100 fromanother device; or (ii) a software application or module within theback-end application computer server 1100 from another softwareapplication, module, or any other source.

In some embodiments (such as shown in FIG. 11), the storage device 1130further stores a claim database 1200, a scoring database 1300, and arule database 1400. Examples of databases that might be used inconnection with the back-end application computer server 1100 will nowbe described in detail with respect to FIGS. 12 through 14. Note thatthe databases described herein are only examples, and additional and/ordifferent information may be stored therein. Moreover, various databasesmight be split or combined in accordance with any of the embodimentsdescribed herein. For example, claim database 1200 and/or scoringdatabase 1300 might be combined and/or linked to each other within theprogram 1115.

Referring to FIG. 12, a table is shown that represents the claimdatabase 1200 that may be stored at the back-end application computerserver 1100 according to some embodiments. The table may include, forexample, entries associated with historical or current STD insuranceclaims. The table may also define fields 1202, 1204, 1206, 1208 for eachof the entries. The fields 1202, 1204, 1206, 1208 may, according to someembodiments, specify: a claim identifier 1202, an injury description1204, a gender 1206, and an age 1208. The claim database 1200 may becreated and updated, for example, based on information electricallyreceived from a historical or current claim data store.

The claim identifier 1202 may be, for example, a unique alphanumericcode identifying a STD insurance claim (e.g., either a historicalinsurance claim or a current insurance claim). The injury description1204, gender 1206, and age 1208 may describe the particular details ofthe STD insurance claim. This information may be used, for example, tocreate a set of search criteria variables when analyzing subgroups ofclaims.

Referring to FIG. 13, a table is shown that represents the scoringdatabase 1300 that may be stored at the back-end application computerserver 1100 according to some embodiments. The table may include, forexample, entries associated with claims and/or subsets of STD insuranceclaims. The table may also define fields 1302, 1304, 1306, 1308 for eachof the entries. The fields 1302, 1304, 1306, 1308 may, according to someembodiments, specify: a claim or claim group identifier 1302, asuccessful outcome indication 1304, an impactability score 1306, and aLTD risk score 1308. The scoring database 1300 may be created andupdated, for example, based on information electrically received fromhistorical and/or current STD insurance claims.

The claim or claim group identifier 1302 may be, for example, a uniquealphanumeric code identifying a particular STD insurance claim or groupof STD insurance claims (e.g., as defined by a set of search criteria).In the case of historical claims, the successful outcome indication 1304might be determined, for example, by an outcome tracker system. Theimpactability score 1306 might be associated with how likely it is thata particular resource will help improve claim resolution. The LTD riskscore 1308 indicates how likely it is that a STD insurance claim willtransition to a LTD claim (and, as a result, might be more likely tobenefit from allocated resources).

Referring to FIG. 14, a table is shown that represents the rule database1400 that may be stored at the back-end application computer server 1100according to some embodiments. The table may include, for example,entries associated with rules are will be used to allocate resources toSTD insurance claims. The table may also define fields 1402, 1404 foreach of the entries. The fields 1402, 1404 may, according to someembodiments, specify: a rule identifier 1402 and logic 1404. The ruledatabase 1400 may be created and updated, for example, based oninformation electrically received from an operator terminal orpredictive model (e.g., that automatically identifies potentialsuccessful subgroups of claims).

The rule identifier 1402 may be, for example, a unique alphanumeric codeidentifying business logic or search criteria that will be used toallocate resources (e.g., as either part of a pilot program or as anongoing strategy). The logic 1404 might represent, for example, a set ofsearch criteria that has been identified as being helpful in connectionwith the allocation of resources to STD insurance claims. The logic 1404might have been identified, for example, by an operator or a predictivemodel.

According to some embodiments, one or more predictive models may be usedto allocate resources. Features of some embodiments associated with apredictive model will now be described by first referring to FIG. 15.FIG. 15 is a partially functional block diagram that illustrates aspectsof a computer system 1500 provided in accordance with some embodimentsof the invention. For present purposes it will be assumed that thecomputer system 1500 is operated by an insurance company (not separatelyshown) for the purpose of supporting an allocation of resources (e.g.,to assign return to work case managers to STD insurance claims, etc.).

The computer system 1500 includes a data storage module 1502. In termsof its hardware the data storage module 1502 may be conventional, andmay be composed, for example, by one or more magnetic hard disk drives.A function performed by the data storage module 1502 in the computersystem 1500 is to receive, store and provide access to both historicaltransaction data (reference numeral 1504) and current transaction data(reference numeral 1506). As described in more detail below, thehistorical transaction data 1504 is employed to train a predictive modelto provide an output that indicates an identified performance metricand/or an algorithm to score results, and the current transaction data1506 is thereafter analyzed by the predictive model. Moreover, as timegoes by, and results become known from processing current transactions(e.g., claim resolution results), at least some of the currenttransactions may be used to perform further training of the predictivemodel. Consequently, the predictive model may thereby appropriatelyadapt itself to changing conditions.

Either the historical transaction data 1504 or the current transactiondata 1506 might include, according to some embodiments, determinate andindeterminate data. As used herein and in the appended claims,“determinate data” refers to verifiable facts such as the an age of abusiness; an automobile type; a policy date or other date; a driver age;a time of day; a day of the week; a geographic location, address or ZIPcode; and a policy number.

As used herein, “indeterminate data” refers to data or other informationthat is not in a predetermined format and/or location in a data recordor data form. Examples of indeterminate data include narrative speech ortext, information in descriptive notes fields and signal characteristicsin audible voice data files.

The determinate data may come from one or more determinate data sources1508 that are included in the computer system 1500 and are coupled tothe data storage module 1502. The determinate data may include “hard”data like a claimant's name, date of birth, social security number,policy number, address, an underwriter decision, etc. One possiblesource of the determinate data may be the insurance company's policydatabase (not separately indicated).

The indeterminate data may originate from one or more indeterminate datasources 1510, and may be extracted from raw files or the like by one ormore indeterminate data capture modules 1512. Both the indeterminatedata source(s) 1510 and the indeterminate data capture module(s) 1512may be included in the computer system 1500 and coupled directly orindirectly to the data storage module 1502. Examples of theindeterminate data source(s) 1510 may include data storage facilitiesfor document images, for text files, and digitized recorded voice files.Examples of the indeterminate data capture module(s) 1512 may includeone or more optical character readers, a speech recognition device(i.e., speech-to-text conversion), a computer or computers programmed toperform natural language processing, a computer or computers programmedto identify and extract information from narrative text files, acomputer or computers programmed to detect key words in text files, anda computer or computers programmed to detect indeterminate dataregarding an individual or claim.

The computer system 1500 also may include a computer processor 1514. Thecomputer processor 1514 may include one or more conventionalmicroprocessors and may operate to execute programmed instructions toprovide functionality as described herein. Among other functions, thecomputer processor 1514 may store and retrieve historical insurancetransaction data 1504 and current transaction data 1506 in and from thedata storage module 1502. Thus the computer processor 1514 may becoupled to the data storage module 1502.

The computer system 1500 may further include a program memory 1516 thatis coupled to the computer processor 1514. The program memory 1516 mayinclude one or more fixed storage devices, such as one or more hard diskdrives, and one or more volatile storage devices, such as RAM devices.The program memory 1516 may be at least partially integrated with thedata storage module 1502. The program memory 1516 may store one or moreapplication programs, an operating system, device drivers, etc., all ofwhich may contain program instruction steps for execution by thecomputer processor 1514.

The computer system 1500 further includes a predictive model component1518. In certain practical embodiments of the computer system 1500, thepredictive model component 1518 may effectively be implemented via thecomputer processor 1514, one or more application programs stored in theprogram memory 1516, and computer stored as a result of trainingoperations based on the historical transaction data 1504 (and possiblyalso data received from a third party). In some embodiments, dataarising from model training may be stored in the data storage module1502, or in a separate computer store (not separately shown). A functionof the predictive model component 1518 may be to determine appropriateresource allocations for a set of STD insurance claims. The predictivemodel component may be directly or indirectly coupled to the datastorage module 1502.

The predictive model component 1518 may operate generally in accordancewith conventional principles for predictive models, except, as notedherein, for at least some of the types of data to which the predictivemodel component is applied. Those who are skilled in the art aregenerally familiar with programming of predictive models. It is withinthe abilities of those who are skilled in the art, if guided by theteachings of this disclosure, to program a predictive model to operateas described herein.

Still further, the computer system 1500 includes a model trainingcomponent 1520. The model training component 1520 may be coupled to thecomputer processor 1514 (directly or indirectly) and may have thefunction of training the predictive model component 1518 based on thehistorical transaction data 1504 and/or information about insuranceclaims. (As will be understood from previous discussion, the modeltraining component 1520 may further train the predictive model component1518 as further relevant data becomes available.) The model trainingcomponent 1520 may be embodied at least in part by the computerprocessor 1514 and one or more application programs stored in theprogram memory 1516. Thus, the training of the predictive modelcomponent 1518 by the model training component 1520 may occur inaccordance with program instructions stored in the program memory 1516and executed by the computer processor 1514.

In addition, the computer system 1500 may include an output device 1522.The output device 1522 may be coupled to the computer processor 1514. Afunction of the output device 1522 may be to provide an output that isindicative of (as determined by the trained predictive model component1518) particular results, insurance claim resolutions, etc. The outputmay be generated by the computer processor 1514 in accordance withprogram instructions stored in the program memory 1516 and executed bythe computer processor 1514. More specifically, the output may begenerated by the computer processor 1514 in response to applying thedata for the current simulation to the trained predictive modelcomponent 1518. The output may, for example, be a numerical estimateand/or likelihood within a predetermined range of numbers. In someembodiments, the output device may be implemented by a suitable programor program module executed by the computer processor 1514 in response tooperation of the predictive model component 1518.

Still further, the computer system 1500 may include an analytic scoringengine model 1524. The analytic scoring engine model 1524 may beimplemented in some embodiments by a software module executed by thecomputer processor 1514. The analytic scoring engine model 1524 may havethe function of facilitating resource allocations for STD insuranceclaims via a portion of the display on the output device 1522. Thus, theanalytic scoring engine model 1524 may be coupled, at leastfunctionally, to the output device 1522. In some embodiments, forexample, the analytic scoring engine model 1524 may report resultsand/or predictions by routing, to an administrator 1528 via an analyticscoring engine platform 1526, a results log and/or automaticallygenerated filter criteria generated by the predictive model component1518. In some embodiments, this information may be provided to anadministrator 1528 who may also be tasked with determining whether ornot the results may be improved (e.g., by further adjusting the models).

Thus, embodiments may provide an automated and efficient way to addressthe need for a consistent and objective determination of how to deployscarce resources in connection with a group benefits employee insuranceplan. Embodiments may also provide a clinical analytics scoring enginetool as a web-based data application that allows group benefitsclinicians to quickly analyze subsets of STD claims to determine whichgroups of claims warrant proactive intervention by medical casemanagers, behavioral health case managers, and return to workcoordinators. Note that insurance enterprise might need to processhundreds of thousands of STD insurance claims annually. Each claim maybe managed by an ability analyst who is responsible for reviewing andadjudicating the claim. In addition to ability analysts, an insuranceenterprise may employ clinicians to provide medical, behavioral, and/orvocational expertise for the claims.

Historically, this group of clinicians has played a reactive role,intervening on claims at the request of the ability analyst. Accordingto some embodiments described herein, the clinical group may take on amore proactive role, using expertise to identify which claims mightpotentially benefit from a medical, behavioral, and/or vocationalclinician. An insurance enterprise might have an extensive store ofhistorical claim data, including clinical intervention rates andoutcomes. The clinical analytics scoring engine tool is designed to helpan insurance enterprise improve group benefits STD claim outcomes bybridging the gap between data analytics and clinical expertise. It mayprovide a user-friendly web-based interface to allow a clinician togenerate hypotheses around proactive intervention and get a statisticalprofile around a given set of claim criteria.

According to some embodiments, the tool accept inputs in the form ofvariables that impact claim outcomes such as: diagnosis category andcode, age at date of disability, surgical procedure code, job class, andclaim complexity (claim segment). Additional variables may be generatedand added to the data set as needed and could be determined via textmining of claim files. The may tool generate a number of key outputsbased on the selected input variables, such as: clinical interventionrate, medical case manager, behavioral health case manager, return towork coordinator, clinical resolution/success rate (based on definedclinical resolutions), clinical impactability, total claims per year,median and mean claim duration days, LTD risk, volatility, and/orlikelihood to exceed duration by 14 days or more.

The following illustrates various additional embodiments of theinvention. These do not constitute a definition of all possibleembodiments, and those skilled in the art will understand that thepresent invention is applicable to many other embodiments. Further,although the following embodiments are briefly described for clarity,those skilled in the art will understand how to make any changes, ifnecessary, to the above-described apparatus and methods to accommodatethese and other embodiments and applications.

Although specific hardware and data configurations have been describedherein, note that any number of other configurations may be provided inaccordance with embodiments of the present invention (e.g., some of theinformation associated with the displays described herein might beimplemented as a virtual or augmented reality display and/or thedatabases described herein may be combined or stored in externalsystems). Similarly, the displays and devices illustrated herein areonly provided as examples, and embodiments may be associated with anyother types of user interfaces. For example, FIG. 16 illustrates ahandheld tablet computer 1600 displaying a resource allocation display1610 according to some embodiments. The resource allocation display 1610might include user-selectable graphical data providing information aboutsearch criteria and/or results that can be selected and/or modified by auser of the handheld computer 1600.

Embodiments described herein may help an insurance enterprise allocatescarce resource in an efficient and effective manner. For example, FIG.17 illustrates an overall enterprise workflow 1700 in accordance withsome embodiments. At S1710, the insurance enterprise may collecthistorical STD insurance claim data. The historical data might becollected, for example, as STD insurance claims are processed, closed,transitioned to LTD claims, etc. At S1720, the system may catalogue“successful” and “unsuccessful” outcomes. For example, an outcometracker system might analyze processed STD insurance claims anddetermine which one meet a clinical definition for “success” for thetype of injury involved, age of claimant, etc.

At S1730, various filter criteria may be defined. For example, anoperator (or group of operators) might wonder if a particular subset ofinsurance claims might especially benefit from being assigned to areturn to work coordinator. At S1740, the system may automaticallycalculate and display impactability scores for the defined filtercriteria. The impactability score might indicate how likely it is that aparticular type of claim will benefit from an allocation or resources.Similarly, at S1750 the system may automatically calculate and displaynegative outcome risk scores for the defined filter criteria. Thenegative outcome risk scores might indicate how beneficial a positiveinfluence would be to the insurance enterprise (e.g., by avoiding a LTDtransition). At S1760, promising filter criteria may be identified. Thatis one or more subsets of historical claims might be identified ashaving relatively high impactability scores and relatively high negativeoutcome risk scores.

At S1770, the identified filter criteria may then be applied to acurrent batch of insurance claims. At S1780, resources may be allocatedin accordance with the recommendations. For example, a return to workcoordinator might be assigned to all of the claims identified at S1770.At S1790, the insurance claims may be processed and an employee's returnto work may be facilitated. In this way, the scarce resources (e.g., thereturn to work coordinator's time) may be allocated to STD insuranceclaims in an effective and efficient manner.

The present invention has been described in terms of several embodimentssolely for the purpose of illustration. Persons skilled in the art willrecognize from this description that the invention is not limited to theembodiments described, but may be practiced with modifications andalterations limited only by the spirit and scope of the appended claims.

What is claimed:
 1. A system to facilitate an allocation of resourcesfor an enterprise system via an automated back-end application computerserver, comprising: (a) a historical request data store containingelectronic records representing a plurality of historical requests and,for each historical request, a set of analysis variables including: arequest description, resource allocation data, and at least one outcomeindication; (b) an automated outcome tracker system computer programmedto: (i) retrieve the electronic records from the historical request datastore, (ii) catalogue a subset of the electronic records, based on theat least one outcome indication for each electronic record, asrepresenting positive outcomes, and (iii) output information about thecatalogued subset of electronic records; (c) the automated back-endapplication computer server, coupled to the automated outcome trackersystem computer, programmed to: (iv) receive, through a distributedcommunication network, a set of analysis filter criteria comprisingindications of a plurality of analysis variables entered via aninteractive graphical user interface display, (v) access the electronicrecords from the historical request data store and receive theinformation about the catalogued subset of electronic records from theautomated outcome tracker system computer, (vi) based on the analysisfilter, resource allocation analysis variables, and catalogued subset ofelectronic records, calculate impactability scores, and (vii) transmitindications of the impactability scores to be provided via theinteractive graphical user interface display; and (d) a communicationport coupled to the back-end application computer server to facilitatean exchange of electronic messages associated with the interactivegraphical user interface display via the distributed communicationnetwork.
 2. The system of claim 1, wherein the automated back-endcomputer server is further programmed to: (viii) based on the analysisfilter, resource allocation analysis variables, and outcome indications,calculate negative outcome risk scores, wherein indications of thenegative outcome risk scores are also transmitted to be provided via theinteractive graphical user interface display.
 3. The system of claim 2,further comprising: (e) a risk allocation tool computer, programmed to:receive the set of analysis filter criteria as meeting a pre-determinedperformance threshold, receive electronic records representing aplurality of current requests and, for each historical request, a set ofanalysis variables, and automatically output a resource allocationrecommendation for the enterprise system in connection with each currentrequest based on the set of analysis filter criteria.
 4. The system ofclaim 2, wherein the enterprise system is associated with a groupbenefits insurance system and the historical requests represent shortterm disability insurance claims.
 5. The system of claim 4, wherein therequest description is associated with at least one of a diagnosiscategory, a diagnosis code, and a surgical procedure code and furtherwherein the resource allocation data is associated with at least one ofa medical case manager, a behavioral health case manager, and a returnto work coordinator.
 6. The system of claim 4, wherein at least oneoutcome indication is associated with a disability duration and thecatalogued subset represents positive outcomes are associated with adefined clinical resolution.
 7. The system of claim 4, wherein the setof analysis variables includes an injury description and furtherincludes claimant data comprising at least one of: an age at date ofdisability, a job class, a gender, and demographic information about theclaimant.
 8. The system of claim 4, wherein the set of analysisvariables includes an injury description and further includes employmentdata comprising at least one of: an inability to contact claimant, anindication claimant has employment issues, a prior history of claims, ahiring date, and a retirement date.
 9. The system of claim 4, whereinthe set of analysis variables includes an injury description and furtherincludes at least one of: a claim volume, a claim complexity segment, adetailed description of a primary disabling diagnosis, a medicaldiagnosis code, an indication of whether a diagnosis is highlysubjective, an indication of multiple diagnoses, a surgery code, anindication of a musculoskeletal diagnosis, an indication of a diabetesdiagnosis, a substance abuse diagnosis, a hypertension diagnosis, and alower limb fracture diagnosis.
 10. The system of claim 4, wherein theimpactability scores are associated with at least one of: medical casemanagers, behavioral health case managers, and return to workcoordinators.
 11. The system of claim 3, wherein the negative outcomerisk scores are associated with relative risks of transitions from shortterm disability insurance claims to long term disability insuranceclaims.
 12. The system of claim 3, wherein the automated back-endapplication computer server is further programmed to: receive, throughthe distributed communication network, an adjusted set of analysisfilter criteria, re-calculate the impactability scores based on theadjusted set of analysis filter criteria, re-calculate the negativeoutcome risk scores based on the adjusted set of analysis filtercriteria, and transmit indications of the re-calculated impactabilityand negative outcome risk scores to be provided via the interactivegraphical user interface display.
 13. The system of claim 3, wherein theset of analysis filter criteria further includes at least one searchterm.
 14. The system of claim 3, wherein the automated back-endapplication computer server is further programmed to calculate clinicalintervention rates and total claim volumes.
 15. The system of claim 3,wherein the automated back-end application computer server is furtherprogrammed to calculate volatility scores associated with relativevariability in claim durations for a specified group of claims ascompared to an overall population of claims.
 16. The system of claim 3,wherein the automated back-end application computer server is furtherprogrammed to calculate at least one of: a median claim duration, a meanclaim duration, a total number of interventions, interventions as apercent of claim volume, successes as a percentage of interventions, amean number of paid benefit days, a median number of paid benefit days,and a standard deviation of claim duration days.
 17. A computerizedmethod to facilitate an allocation of resources for an enterprise systemvia an automated back-end application computer server, comprising:retrieving, by an automated outcome tracker system computer from ahistorical request data store, electronic records representing aplurality of historical requests and, for each historical request, a setof analysis variables including: a request description, resourceallocation data, and at least one outcome indication; cataloguing, bythe automated outcome tracker system computer, a subset of theelectronic records, based on the at least one outcome indication foreach electronic record, as representing positive outcomes; outputting,from the automated outcome tracker system computer, information aboutthe catalogued subset of electronic records; receiving, by the automatedback-end application computer server through a distributed communicationnetwork, a set of analysis filter criteria comprising indications of aplurality of analysis variables entered via the interactive graphicaluser interface display; accessing, by the back-end application computerserver, the electronic records from the historical request data storeand receiving the information about the catalogued subset of electronicrecords from the automated outcome tracker system computer; based on theanalysis filter, resource allocation analysis variables, and cataloguedsubset of electronic records, calculating impactability scores; based onthe analysis filter, resource allocation analysis variables, and outcomeindications, calculating negative outcome risk scores; and transmitting,from the back-end application computer server, indications of theimpactability scores and negative outcome risk scores to be provided viathe interactive graphical user interface display.
 18. The method ofclaim 17, wherein the enterprise system is associated with a groupbenefits insurance system, the historical requests represent short termdisability insurance claims, the request description is associated witha diagnosis category, the resource allocation data is associated with amedical case manager, at least one outcome indication is associated witha disability duration, the catalogued subset representing positiveoutcomes are associated with a defined clinical resolution, and thenegative outcome risk scores are associated with relative risks oftransitions from short term disability insurance claims to long termdisability insurance claims.
 19. A system to facilitate an allocation ofresources for an enterprise system via an automated back-end applicationcomputer server, comprising: (a) a historical request data storecontaining electronic records representing a plurality of historicalrequests and, for each historical request, a set of analysis variablesincluding: a request description, resource allocation data, and at leastone outcome indication; (b) an automated outcome tracker system computerprogrammed to: (i) retrieve the electronic records from the historicalrequest data store, (ii) catalogue a subset of the electronic records,based on the at least one outcome indication for each electronic record,as representing positive outcomes, and (iii) output information aboutthe catalogued subset of electronic records; (b) the automated back-endapplication computer server, coupled to the automated outcome trackersystem computer, programmed to: (iv) receive, through a distributedcommunication network, a set of analysis filter criteria comprisingindications of a plurality of analysis variables entered via theinteractive graphical user interface display, (v) access the electronicrecords from the historical request data store and receive theinformation about the catalogued subset of electronic records from theautomated outcome tracker system computer, (vi) based on the analysisfilter, resource allocation analysis variables, and catalogued subset ofelectronic records, calculate impactability scores, (vii) based on theanalysis filter, resource allocation analysis variables, and outcomeindications, calculate negative outcome risk scores, and (viii) transmitindications of the impactability scores and negative outcome risk scoresto be provided via the interactive graphical user interface display; and(c) a communication port coupled to the back-end application computerserver to facilitate an exchange of electronic messages associated withthe interactive graphical user interface display via the distributedcommunication network, wherein the graphical user interface displayincludes: visually perceptible elements indicating the analysis filer,information associated with the impactability scores proximate to thevisually perceptible elements indicating the analysis filter, andinformation associated with the negative outcome risk scores proximateto the visually perceptible elements indicating the analysis filter. 20.The system of claim 19, wherein the enterprise system is associated witha group benefits insurance system, the historical requests representshort term disability insurance claims, the request description isassociated with a diagnosis category, the resource allocation data isassociated with a medical case manager, at least one outcome indicationis associated with a disability duration, the catalogued subsetrepresenting positive outcomes are associated with a defined clinicalresolution, and the negative outcome risk scores are associated withrelative risks of transitions from short term disability insuranceclaims to long term disability insurance claims.